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In-The-Wild deepfake detection using adaptable CNN models with visual class activation mapping for improved accuracy

Deepfake technology has become increasingly sophisticated in recent years, making detecting fake images and videos challenging. This paper investigates the performance of adaptable convolutional neural network (CNN) models for detecting Deepfakes. In-the-wild OpenForensics dataset was used to evalua...

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Main Authors: Muhammad Salihin, Saealal, Mohd Zamri, Ibrahim, Mohd Ibrahim, Shapiai, Norasyikin, Fadilah
Format: Conference or Workshop Item
Language:English
English
Published: 2023
Subjects:
T Technology (General)
TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://umpir.ump.edu.my/id/eprint/38714/1/In-the-wild%20deepfake%20detection%20using%20adaptable%20CNN%20models.pdf
http://umpir.ump.edu.my/id/eprint/38714/2/In-The-Wild%20deepfake%20detection%20using%20adaptable%20CNN%20models%20with%20visual%20class%20activation%20mapping%20for%20improved%20accuracy_ABS.pdf
http://umpir.ump.edu.my/id/eprint/38714/
https://doi.org/10.1109/ICCCI59363.2023.10210096
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spelling my.ump.umpir.387142023-11-06T04:24:15Z http://umpir.ump.edu.my/id/eprint/38714/ In-The-Wild deepfake detection using adaptable CNN models with visual class activation mapping for improved accuracy Muhammad Salihin, Saealal Mohd Zamri, Ibrahim Mohd Ibrahim, Shapiai Norasyikin, Fadilah T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Deepfake technology has become increasingly sophisticated in recent years, making detecting fake images and videos challenging. This paper investigates the performance of adaptable convolutional neural network (CNN) models for detecting Deepfakes. In-the-wild OpenForensics dataset was used to evaluate four different CNN models (DenseNet121, ResNet18, SqueezeNet, and VGG11) at different batch sizes and with various performance metrics. Results show that the adapted VGG11 model with a batch size of 32 achieved the highest accuracy of 94.46% in detecting Deepfakes, outperforming the other models, with DenseNet121 as the second-best performer achieving an accuracy of 93.89% with the same batch size. Grad-CAM techniques are utilized to visualize the decision-making process within the models, aiding in understanding the Deepfake classification process. These findings provide valuable insights into the performance of different deep learning models and can guide the selection of an appropriate model for a specific application. 2023 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/38714/1/In-the-wild%20deepfake%20detection%20using%20adaptable%20CNN%20models.pdf pdf en http://umpir.ump.edu.my/id/eprint/38714/2/In-The-Wild%20deepfake%20detection%20using%20adaptable%20CNN%20models%20with%20visual%20class%20activation%20mapping%20for%20improved%20accuracy_ABS.pdf Muhammad Salihin, Saealal and Mohd Zamri, Ibrahim and Mohd Ibrahim, Shapiai and Norasyikin, Fadilah (2023) In-The-Wild deepfake detection using adaptable CNN models with visual class activation mapping for improved accuracy. In: 2023 5th International Conference on Computer Communication and the Internet, ICCCI 2023, 23-25 June 2023 , Fujisawa. pp. 9-14.. ISBN 979-835032695-6 https://doi.org/10.1109/ICCCI59363.2023.10210096
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
English
topic T Technology (General)
TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
spellingShingle T Technology (General)
TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
Muhammad Salihin, Saealal
Mohd Zamri, Ibrahim
Mohd Ibrahim, Shapiai
Norasyikin, Fadilah
In-The-Wild deepfake detection using adaptable CNN models with visual class activation mapping for improved accuracy
description Deepfake technology has become increasingly sophisticated in recent years, making detecting fake images and videos challenging. This paper investigates the performance of adaptable convolutional neural network (CNN) models for detecting Deepfakes. In-the-wild OpenForensics dataset was used to evaluate four different CNN models (DenseNet121, ResNet18, SqueezeNet, and VGG11) at different batch sizes and with various performance metrics. Results show that the adapted VGG11 model with a batch size of 32 achieved the highest accuracy of 94.46% in detecting Deepfakes, outperforming the other models, with DenseNet121 as the second-best performer achieving an accuracy of 93.89% with the same batch size. Grad-CAM techniques are utilized to visualize the decision-making process within the models, aiding in understanding the Deepfake classification process. These findings provide valuable insights into the performance of different deep learning models and can guide the selection of an appropriate model for a specific application.
format Conference or Workshop Item
author Muhammad Salihin, Saealal
Mohd Zamri, Ibrahim
Mohd Ibrahim, Shapiai
Norasyikin, Fadilah
author_facet Muhammad Salihin, Saealal
Mohd Zamri, Ibrahim
Mohd Ibrahim, Shapiai
Norasyikin, Fadilah
author_sort Muhammad Salihin, Saealal
title In-The-Wild deepfake detection using adaptable CNN models with visual class activation mapping for improved accuracy
title_short In-The-Wild deepfake detection using adaptable CNN models with visual class activation mapping for improved accuracy
title_full In-The-Wild deepfake detection using adaptable CNN models with visual class activation mapping for improved accuracy
title_fullStr In-The-Wild deepfake detection using adaptable CNN models with visual class activation mapping for improved accuracy
title_full_unstemmed In-The-Wild deepfake detection using adaptable CNN models with visual class activation mapping for improved accuracy
title_sort in-the-wild deepfake detection using adaptable cnn models with visual class activation mapping for improved accuracy
publishDate 2023
url http://umpir.ump.edu.my/id/eprint/38714/1/In-the-wild%20deepfake%20detection%20using%20adaptable%20CNN%20models.pdf
http://umpir.ump.edu.my/id/eprint/38714/2/In-The-Wild%20deepfake%20detection%20using%20adaptable%20CNN%20models%20with%20visual%20class%20activation%20mapping%20for%20improved%20accuracy_ABS.pdf
http://umpir.ump.edu.my/id/eprint/38714/
https://doi.org/10.1109/ICCCI59363.2023.10210096
_version_ 1822923730321408000
score 13.232414

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